OBJECTIVE: Obesity rates in young children are increasing, and decreased physical activity is likely to be a major contributor to this trend. Studies of physical activity in young children are limited by the lack of valid and acceptable measures. The purpose of this study was to calibrate and validate the ActiGraph accelerometer for use with 3- to 5-year-old children. RESEARCH METHODS AND PROCEDURES: Thirty preschool children wore an ActiGraph accelerometer (ActiGraph, Fort Walton Beach, FL) and a Cosmed portable metabolic system (Cosmed, Rome, Italy) during a period of rest and while performing three structured physical activities in a laboratory setting. Expired respiratory gases were collected, and oxygen consumption was measured on a breath-by-breath basis. Accelerometer data were collected at 15-second intervals. For cross-validation, the same children wore the same instruments while participating in unstructured indoor and outdoor activities for 20 minutes each at their preschool. RESULTS: In calibrating the accelerometer, the correlation between Vo(2) (ml/kg per min) and counts was r = 0.82 across all activities. The only significant variable in the prediction equation was accelerometer counts (R(2) = 0.90, standard error of the estimate = 4.70). In the cross-validation, the intraclass correlation coefficient between measured and predicted Vo(2) was R = 0.57 and the Spearman correlation coefficient was R = 0.66 (p < 0.001). Cut-off points for moderate- and vigorous-intensity physical activity were identified at 420 counts/15 s (Vo(2) = 20 mL/kg per min) and 842 counts/15 s (Vo(2) = 30 mL/kg per min), respectively. When these cutpoints were applied to the cross-validation data, percentage agreement, kappa, and modified kappa for moderate activity were 0.69, 0.36, and 0.38, respectively. For vigorous activity, the same measures were 0.81, 0.13, and 0.62. DISCUSSION: Accelerometer counts were highly correlated with Vo(2) in young children. Accelerometers can be appropriately used as a measure of physical activity in this population.
OBJECTIVE: Obesity rates in young children are increasing, and decreased physical activity is likely to be a major contributor to this trend. Studies of physical activity in young children are limited by the lack of valid and acceptable measures. The purpose of this study was to calibrate and validate the ActiGraph accelerometer for use with 3- to 5-year-old children. RESEARCH METHODS AND PROCEDURES: Thirty preschool children wore an ActiGraph accelerometer (ActiGraph, Fort Walton Beach, FL) and a Cosmed portable metabolic system (Cosmed, Rome, Italy) during a period of rest and while performing three structured physical activities in a laboratory setting. Expired respiratory gases were collected, and oxygen consumption was measured on a breath-by-breath basis. Accelerometer data were collected at 15-second intervals. For cross-validation, the same children wore the same instruments while participating in unstructured indoor and outdoor activities for 20 minutes each at their preschool. RESULTS: In calibrating the accelerometer, the correlation between Vo(2) (ml/kg per min) and counts was r = 0.82 across all activities. The only significant variable in the prediction equation was accelerometer counts (R(2) = 0.90, standard error of the estimate = 4.70). In the cross-validation, the intraclass correlation coefficient between measured and predicted Vo(2) was R = 0.57 and the Spearman correlation coefficient was R = 0.66 (p < 0.001). Cut-off points for moderate- and vigorous-intensity physical activity were identified at 420 counts/15 s (Vo(2) = 20 mL/kg per min) and 842 counts/15 s (Vo(2) = 30 mL/kg per min), respectively. When these cutpoints were applied to the cross-validation data, percentage agreement, kappa, and modified kappa for moderate activity were 0.69, 0.36, and 0.38, respectively. For vigorous activity, the same measures were 0.81, 0.13, and 0.62. DISCUSSION: Accelerometer counts were highly correlated with Vo(2) in young children. Accelerometers can be appropriately used as a measure of physical activity in this population.
Authors: Joanna Buscemi; Kristoffer S Berlin; Tiffany M Rybak; Linda A Schiffer; Angela Kong; Melinda R Stolley; Lara Blumstein; Angela Odoms-Young; Marian L Fitzgibbon Journal: J Pediatr Psychol Date: 2015-12-30
Authors: Shari L Barkin; Archana P Lamichhane; Jorge A Banda; Meghan M JaKa; Maciej S Buchowski; Kelly R Evenson; Shrikant I Bangdiwala; Charlotte Pratt; Simone A French; June Stevens Journal: Am J Prev Med Date: 2017-01-09 Impact factor: 5.043
Authors: Sharon E Taverno Ross; Patricia I Documet; Russell R Pate; Ivonne Smith-Tapia; Lisa M Wisniewski; Bethany B Gibbs Journal: Transl J Am Coll Sports Med Date: 2017-07-15
Authors: Marsha Dowda; William H Brown; Kerry L McIver; Karin A Pfeiffer; Jennifer R O'Neill; Cheryl L Addy; Russell R Pate Journal: Pediatrics Date: 2009-02 Impact factor: 7.124